Large organizations, such as commercial organizations, financial organizations or public safety organizations conduct numerous interactions with customers, users, suppliers or other persons on a daily basis. Many of these interactions are vocal, or at least comprise a vocal component, such as an audio part of a video or face-to-face interaction. These interactions can contain various levels of sentiment expressed by either the customer or the agent. Sentiment can be positive, such as happiness, satisfaction, contentment, amusement, or other positive feelings of the speaker, or negative, such as anger, disappointment, resentment, irritation, or other negative feelings.
Detecting interactions that contain sentiment can produce high value to an organization. For example, a customer retention representative or a supervisor can initiate a return call to a customer whose call contained negative sentiments in order to improve customer satisfaction.
In current systems, due to the low percentage of calls containing sentiment out of all calls handled in a contact center, detection of such calls is a labor-intensive task, and one must hear a large amount of calls in order to encounter enough relevant interactions.
There is thus a need in the art for a system and method that can detect interactions having sentiment with high accuracy.